SpikeReg: Energy-Efficient 3D Deformable Medical Image Registration with Spiking Neural Networks
For medical image registration, this work shows that dense geometric prediction can be performed with sparse event-driven computation, enabling neuromorphic hardware deployment.
SpikeReg introduces a spiking U-Net for 3D brain MRI registration, achieving Dice 0.7474 (no significant difference from ANN teacher at 0.7480) with 12.8% spike rate and 55.5× projected energy reduction.
Deformable medical image registration aligns anatomical structures across images but remains computationally dense at 3D resolution. Spiking neural networks (SNNs) offer sparse event-driven computation, yet have not been systematically studied for deformable medical image registration. We introduce SpikeReg, a spiking U-Net for 3D brain MRI registration. SpikeReg is initialized from an analog ANN registration teacher, converted by layer-wise weight transfer and activation-percentile threshold calibration, and fine-tuned with a surrogate-gradient objective combining local cross-correlation, diffusion regularization, and spike-rate sparsity. On the OASIS Learn2Reg validation split ($19$ image pairs), SpikeReg reaches Dice $0.7474 \pm 0.032$, with no significant paired Dice difference from the ANN teacher ($0.7480 \pm 0.037$, $p = 0.67$), at a $12.8\%$ mean spike rate and a $55.5\times$ projected arithmetic-energy reduction under an event-sparse SynOps/MAC proxy relative to the dense-ANN baseline. We additionally report two negative findings: displacement distillation from the ANN teacher hurts performance, and ANN teachers trained with a label-Dice loss fail to transfer through rate-code conversion. Together these results show that dense geometric prediction can be performed under sparse event-driven computation, opening a path toward neuromorphic medical image registration.